INFORMATION-THEORETIC MEASURE AND PRINCIPLE OF MAXIMUM ENTROPY
Let P=(p1 , p2 ,…,pn), be a probability distribution, then the first important measure of information was given by a pioneer Communication Engineer C.E. Shannon  in 1948. The amount of information is that amount by which the uncertainty in a situation characterized by a probability distribution ‘P’ is reduced, when it is known that which outcome will occur. The problem can be reduced in that of finding uncertainty associated with a probability distribution ‘P’. On the basis of some plausible postulates the measure of uncertainty of ‘P’ is deduced as
H (P) = - Sum(pi * ln(pi)) (1)
H(P) is known as a measure of entropy. The word entropy stands for ‘uncertainty’. H(P) satisfies most of the useful properties as, non-negativity, concavity, additivity, increasing with number of outcomes, maximum for uniform distribution, minimum for degenerate distribution and minimum value is zero etc. required to be satisfied by a measure of entropy. Here information and uncertainty are intrinsically related as
Information gained == uncertainty removed
Later E.T. Jaynes  used this measure by enunciating his Principle of Maximum Entropy (PME), according to which, out of all the probability distributions given, one should choose that probability distribution which maximizes H (P) and satisfy all the given constraints. In the absence of any constraints on pi’s except natural constraints, the Maximum Entropy Probability Distribution (MEPD) is a uniform probability distribution. Further if additional information in terms of simple statistical moments of a random variable is prescribed, then we will get a probability distribution, which is ‘closest’ to a uniform probability distribution. In this sense, principle of maximum entropy is a powerful tool to analyse a situation, when partial information in terms of simple statistical moments is prescribed.
My QUESTION : HOW CAN THIS POWERFUL PRINCIPLE BE USED FOR PRODUCT DESIGN AND SYSTEM ANALYSIS?